Automatic Classification of Clean and Noisy Offline Handwritten Signatures Using Hybrid Machine Learning
TL;DR
A new approach is presented here that integrates traditional handcrafted texture features such as Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix (GLCM) with deep learning models with deep learning models.
Abstract
The automatic partitioning of handwritten signatures into clean and noisy is crucial to reliable authentication in documents. A new approach is presented here that integrates traditional handcrafted texture features such as Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrix (GLCM) with deep learning models. We have performed experiments on an offline signature dataset DocSign V2 comprising 2,400 grayscale signature images (50 users, equal number of clean and noisy signatures) created for this work. Eight conventional classifiers are tested on individual and combinations of features. The performance of a CNN built for the task and also four transfer learning models - EfficientNetB0, ResNet50, MobileNetV2, and DenseNet121 - are compared. The experiments show the best result of 97.5% was achieved with the HOG-LBP+AdaBoost combination. This is better than the best of deep learning models, MobileNetV2, which reached 95%. This demonstrates that, particularly for small datasets, carefully designed handcrafted features can be used to effectively classify clean and noisy signatures automatically. Moreover, the proposed system can also be used as a post-processing module to enhance signatures for other analysis or verification.